1st Edition

Biostatistics: A Computing Approach

By Stewart Anderson Copyright 2012
    328 Pages 65 B/W Illustrations
    by Chapman & Hall

    The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding.

    As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work.

    Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.

    Preface

    Review of Topics in Probability and Statistics
    Introduction to Probability
    Conditional Probability
    Random Variables
    The Uniform distribution
    The Normal distribution
    The Binomial Distribution
    The Poisson Distribution
    The Chi–Squared Distribution
    Student’s t–distribution
    The F-distribution
    The Hypergeometric Distribution
    The Exponential Distribution
    Exercises

    Use of Simulation Techniques
    Introduction
    What can we accomplish with simulations?
    How to employ a simple simulation strategy
    Generation of Pseudorandom Numbers
    Generating Discrete and Continuous random variables
    Testing Random Number Generators
    A Brief Note on the Efficiency of Simulation Algorithms
    Exercises

    The Central Limit Theorem
    Introduction
    The Strong Law of Large Numbers
    The Central Limit Theorem
    Summary of the Inferential Properties of the Sample Mean
    Appendix: Program Listings
    Exercises

    Correlation and Regression
    Introduction
    Pearson’s Correlation Coefficient
    Simple Linear Regression
    Multiple Regression
    Visualization of Data
    Model Assessment and Related Topics
    Polynomial Regression
    Smoothing Techniques
    Appendix: A Short Tutorial in Matrix Algebra
    Exercises

    Analysis of Variance
    Introduction
    One–Way Analysis of Variance
    General Contrast
    Multiple Comparisons Procedures
    Gabriel’s method
    Dunnett’s Procedure
    Two-Way Analysis of Variance: Factorial Design
    Two-Way Analysis of Variance: Randomized Complete Blocks
    Analysis of Covariance
    Exercises

    DiscreteMeasures of Risk
    Introduction
    Odds Ratio (OR) and Relative Risk (RR)
    Calculating risk in the presence of confounding
    Logistic Regression
    Using SAS and R for Logistic Regression
    Comparison of Proportions for Paired Data
    Exercises

    Multivariate Analysis
    The Multivariate Normal Distribution
    One and Two Sample Multivariate Inference
    Multivariate Analysis of Variance
    Multivariate Regression Analysis
    Classification Methods
    Exercises

    Analysis of Repeated Measures Data
    Introduction
    Plotting Repeated Measures Data
    Univariate Approaches for the Analysis of Repeated Measures Data
    Covariance Pattern Models
    Multivariate Approaches
    Modern Approaches for the Analysis of Repeated Measures Data
    Analysis of Incomplete Repeated Measures Data
    Exercises

    NonparametricMethods
    Introduction
    Comparing Paired Distributions
    Comparing Two Independent Distributions
    Kruskal–Wallis Test
    Spearman’s rho
    The Bootstrap
    Exercises

    Analysis of Time to Event Data
    Incidence Density (ID)
    Introduction to Survival Analysis
    Estimation of the Survival Curve
    Estimating the Hazard Function
    Comparing Survival in Two Groups
    Cox Proportional Hazards Model
    Cumulative Incidence
    Exercises

    Sample size and power calculations
    Sample sizes and power for tests of normally distributed data
    Sample size and power for Repeated Measures Data
    Sample size and power for survival analysis
    Constructing Power Curves
    Exercises

    Appendix A: Using SAS
    Introduction
    Data input in SAS
    Some Graphical Procdures: PROC PLOT and PROC CHART
    Some Simple Data Analysis Procedures
    Diagnosing errors in SAS programs
    Exercises

    Appendix B: Using R
    Introduction
    Getting started
    Input/Output
    Some Simple Data Analysis Procedures
    Using R for plots
    Comparing an R–session to a SAS session
    Diagnosing problems in R programs
    Exercises

    References

    Index

    Biography

    Stewart Anderson

    "The book presents important topics in biostatistics alongside examples provided in the programming languages SAS and R. … The book covers many relevant topics every student should know in a way that it makes it easy to follow … each chapter provides exercises encouraging the reader to deepen her/his understanding. I really like that the theory is presented in a clear manner without interruptions of example programs. Instead, the programs are always presented at the end of a section. … this book can serve as a good start for the more statistics inclined students who haven’t yet recognized that in order to become a good biostatistician, you need to be able to write your own code. … I can recommend to all serious students who want to get a thorough start into this field."
    —Frank Emmert-Streib, Queen’s University Belfast, CHANCE, August 2013